Array of functions - is there a way to avoid allocations performance penalty?

[chakravala]

In my case, it is needed for encoding the metric at a tangent space of a Riemannian manifold in differential geometry

[ANN] DirectSum.jl, AbstractTensors.jl : dispatch on VectorBundle <: Manifold{n}

The DirectSum package has a tangent bundle encoding based on the following: Let T^dV\in\text{Vect}_\mathbb K be a VectorBundle<:Manifold of rank n, T^dV = (n,p,g,d),\, n \in\mathbb N,\, g :V\times V\rightarrow\mathbb K, \, d \in\mathbb Z ::VectorBundle{n,p,g,d} where {n,p,g,d} byte-encoded p specifies the null-basis from the projective split of V, g is a bilinear form that specifies the metric of the space, d is an integer specifying the order of the tangent bundle. \bigoplus T^{d…

The Hodge star requires evaluating a volume element of a submanifold using the metric at a particular point, which is currently considered constant and will be generalized to a FunctionWrapper next.